: Explainable Data Quality & Reliability Tool for Open Datasets
The Explainable Data Quality and Reliability Tool is user friendly web site which enables students, researchers and/or even developers to check the quality of data sets before using them in projects or machine learning models. The user can post a dataset, and the system will automatically scan the data and identify issues like missing data, duplication, wrong type of data, inconsistent formatting, and unrealistic data. The tool will obtain a score of data health, visualize the identified problems, present them in easy language, and provide auto-cleaning recommendations to enhance the reliability of the dataset. This prevents users at getting the wrong results and developing accurate and reliable projects based on data.
Title: Explainable Data Quality & Reliability Tool for Open Datasets
Today, many students, researchers, and developers download datasets from the internet to use in their projects, research, and machine learning models.
These datasets look correct on the outside, but inside they often contain
hidden problems such as:
empty cells (missing values)
repeated rows (duplicates)
wrong data types (text instead of numbers)
inconsistent formats (different date styles)
strange or unrealistic values
Most beginners do not know how to check whether the dataset is clean or reliable. They directly use the data, and because of that:
their results become wrong
their research conclusions become incorrect
their models give inaccurate predictions
their projects lose reliability
Although some data quality tools already exist, they are mostly made for professional data engineers and require technical knowledge. They are difficult for beginners to understand and use.
There is currently no simple, beginner-friendly, open-source tool that:
automatically checks dataset quality,
explains problems in easy language,
shows how those problems affect results, and
helps users safely improve the dataset.
This creates a gap between open data availability and data reliability awareness.